Heavy metals and prostate cancer: a new study with new findings

Heavy metals and prostate cancer: a new study with new findings | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Heavy metals and prostate cancer: a new study with new findings Donatella Coradduzza, Antonella Congiargiu, Andrea Sanna, Biagio Lorenzo, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5822110/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Heavy metals influence the development of several health conditions, including inflammation and cancer. This study investigates the relationship between heavy metal concentrations in plasma and urine, and the presence of benign prostatic hyperplasia (BPH), precancerous lesions (PL), and prostate cancer (PC). The influence of age, total PSA levels, hemoglobin concentrations, and the monocyte-to-lymphocyte ratio (MLR) is also analyzed across the three groups: BPH, PL, and PC. Our findings reveal significant differences in vanadium and antimony concentrations in plasma, suggesting a potential role in prostate disease pathophysiology. Notably, lower plasma antimony concentrations are associated with an increased risk of PC, while plasma vanadium concentrations are significantly higher in the PL group. Regression analysis further supports the association between heavy metal concentrations and the risk of PL and PC, highlighting the potential of vanadium and copper as biomarkers or therapeutic targets for prostate health. The study also explores the impact of lead exposure on prostate cancer risk, revealing a significant association between urine lead concentration and PC. These findings underscore the complex interaction between heavy metal concentrations and prostate disease risk, emphasizing the need for further research to elucidate underlying mechanisms and explore therapeutic interventions. Biological sciences/Biochemistry Biological sciences/Cancer Biological sciences/Chemical biology Health sciences/Biomarkers Health sciences/Health care Health sciences/Oncology Health sciences/Pathogenesis Health sciences/Risk factors Health sciences/Urology Physical sciences/Chemistry biomarkers prostate cancer heavy metal cancer risk inductively coupled plasma mass spectrometry (ICP-MS) Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Prostate cancer (PC) is the second most common cancer in men, with over a million cases diagnosed globally in 2018, representing 15% of all cancers [ 1 – 3 ]. The Lancet Commission has recently published projections indicating a significant increase in PC cases from 1.4 million in 2020 to 2.9 million by 2040, particularly in low and middle-income countries [ 4 ]. This increase cannot be prevented with lifestyle changes or public health interventions only, highlighting the need for alternative strategies [ 5 , 6 ]. The Commission emphasizes the importance of improving early diagnosis, developing more effective treatments, and implementing educational programs that shift attention from the treatment of advanced disease to that of the early stage, underscoring the urgency of timely intervention. It is also essential to intensify research to better understand PC causes and development [ 7 – 9 ]. The burden of PC is mainly attributed to several factors, including family history, hormones, ethnicity, aging, and inflammatory states [ 10 – 13 ]. The observed incidence increase is also associated with transitioning from traditional diets rich in fibres and carbohydrates to Westernized diets characterized by a higher concentration of processed red meat and saturated fats [ 14 ]. Such transitioning is influenced by economic growth, contributing to reduced physical activity and increased prevalence of overweight and obesity [ 15 ]. According to the World Cancer Research Fund and the American Cancer Society, up to 16% of PC cases could be prevented by eliminating unhealthy diets and reducing physical inactivity and obesity, while keeping other risk factors unchanged [ 16 – 20 ]. Most PC cases are diagnosed between the ages of 50 and 79. Given the increasing life expectancy, the burden of PC has become a health priority [ 21 ]. As previously mentioned, chronic inflammation plays a critical role in the development of PC. Exposure to heavy metals may favour chronic inflammation [ 22 , 23 ]. Heavy metals are not univocally defined, as their classification can be also based on their physio-chemical properties [ 24 ]. Broadly speaking, they include the d- and f-block transition elements, together with p-block metals and some non-metals like selenium and arsenic [ 25 ]. Their biological role can vary from essential to toxic. Essential trace elements, like iron, copper, cobalt, manganese, zinc, chromium, vanadium, nickel and molybdenum are crucial for enzyme activity, immune regulation, protection from oxidative damage, genomic stability, apoptosis, and cell signalling. Other metals such as cadmium [ 26 ], mercury and lead are toxic [ 27 ] (included in Group 1 substances carcinogenic to humans, or suspected to be). However, even essential metals can be toxic if present in high concentrations [ 28 ]. These elements can accumulate in the body through exposure to industrial, environmental, or food substances [ 29 ]. The accumulation of heavy metals can lead to oxidative damage, which has been associated with a range of diseases, including cancer [ 23 , 30 ]. Besides inflammation and oxidative damage, epidemiological studies have shown that exposure to heavy metals can influence the endocrine system, which regulates growth, development, metabolism, and reproduction [ 31 – 33 ]. This can impact the progression of cancer, including PC. Literature reports a number of studies carried out to identify and quantify heavy elements in biological samples of cancer patients (blood, urine, hair, nail, biopsies, etc.) in order to clarify the relationship between the presence of these pollutants in the organism and the risk of developing the disease. We have recently examined these studies and reunited them in a review [ 23 ] that shows how the results obtained are not always univocal, as the possibility of sound conclusions is limited by the heterogeneity of the data collected in terms of biological samples examined, methodologies, and cohorts of patients selected. Therefore, we decided to add further evidence to these findings by carrying out our own research on this topic. Our study sought to quantify the concentrations of different metals in blood and urine samples of individuals undergoing diagnostic assessment for prostate pathology. Specifically, we investigated associations between such concentrations and different types of prostate pathology, including PC [ 34 ]. This research, conducted with patients from the Urology Clinic at the University Hospital of Sassari (Italy), aims to provide critical insights into the potential role of heavy metal exposure in the aetiology and progression of prostate disorders, thereby advancing our understanding of its implications for prostate health and disease management. Results Subject characteristics From September 2021 to December 2022, a total of 156 participants, all from Sardinia (Italy), were enrolled. The cohort included 61 subjects with BPH, 15 with PL, and 78 with confirmed PC. Age and total PSA, according to the guidelines [ 35 ], were significantly different across the three groups, with PC patients being older (BPH median age = 68; PL = 69; PC = 74; p = 0.002) and having higher total PSA concentrations than the other two groups (BPH median total PSA = 5.9 ng/mL; PL = 4.4 ng/mL; PC = 7.5 ng/mL; p = 0.0002). Hemoglobin concentrations were also significantly lower in PL and PC patients (BPH median RBC = 15 g/dl; PL = 14 g/dl; PC = 14 g/dl; p = 0.019), while other CBC parameters did not differ across the study groups. Among the CBC inflammatory indices, only the monocytes-to-lymphocytes ratio (MLR) was increased in PL and PC patients (BPH mean MLR = 0.22; PL mean MLR = 0.3; PC mean MLR = 0.25; Table 1 ). Statistically significant differences were observed in plasma concentrations of manganese and antimony across the three groups (p = 0.049 and 0.034, respectively), while no between-group differences were observed in urinary concentrations of any heavy metal. These findings were also illustrated in heat maps depicting heavy metal concentrations in plasma and urine (Table 2 , Figs. 1 and 2 ). Additionally, urinary iron, copper, zinc, selenium, arsenic, molybdenum, cadmium, and antimony concentrations were significantly lower than plasma concentrations in all three groups. By contrast, barium urinary concentrations were significantly lower than plasmatic ones only in the BPH group, as reflected in Table 2 . No significant differences were observed for manganese concentrations between plasma and urine across the groups. (Table 2 ). Adjusted Probit Regression Analysis of Metal Concentrations in Plasma and Urine and Their Associations with Prostate Pathologies Probit regression analyses were conducted to evaluate the odds ratio (OR) and 95% confidence interval (CI) for the association between plasma or urinary concentrations of individual heavy metals and the presence of precancerous lesions or prostate cancer, with the benign prostatic hyperplasia group serving as a control (Tables 3 and 4 ). Elevated plasma vanadium concentrations were significantly associated with PL (OR = 11.67; 95% CI: 0.72–22.63; p = 0.04), with a non-significant trend for a similar association in PC patients (OR = 7.92; 95% CI: -1.04–16.87; p = 0.08). In contrast, lower plasma antimony concentrations were significantly associated with PC (OR = -0.25; 95% CI: -0.43 to -0.08; p < 0.001). No significant associations were observed between urinary heavy metal concentrations and PL, but there was a trend for an association between urinary copper and PC (OR = 0.03; 95% CI: 0.00–0.07; p = 0.06) and a significant association for urinary lead and PC (OR = 0.28; 95% CI: 0.02–0.54; p = 0.03). Significant or trend-level associations observed in single-metal regression models were further evaluated in bivariate probit regression analyses adjusted for potential confounders, including age, total PSA, and CBC parameters. Plasma Vanadium and PL After adjustment, elevated plasma vanadium concentrations remained significantly associated with PL when corrected for RBC (OR = 13.10; 95% CI: 1.39–24.81; p = 0.03), HGB (OR = 11.93; 95% CI: 0.69–23.17; p = 0.04), lymphocytes (OR = 11.87; 95% CI: 0.58–23.16; p = 0.04), and age (OR = 12.35; 95% CI: 0.99–23.70; p = 0.03; Table S1 ). No significant associations were observed after adjustments for systemic inflammatory indices such as MLR or SIRI (p > 0.10). These results suggest a robust relationship between plasma vanadium and PL. Plasma Vanadium and PC In contrast, no significant associations were observed between plasma vanadium and PC after adjustment for confounders (Table S2). Adjustment for age (OR = 5.40; 95% CI: -3.85–14.65; p = 0.25), PSA (OR = 6.96; 95% CI: -3.27–17.19; p = 0.18), and hematological parameters such as RBC (OR = 7.75; 95% CI: -2.05–17.54; p = 0.12) did not yield significant results. Plasma Antimony and PC Plasma antimony concentrations were consistently and significantly associated with PC across all adjusted models (Table S3). The association remained significant after adjustment for age (OR = -0.28; 95% CI: -0.46 to -0.09; p < 0.001), PSA (OR = -0.26; 95% CI: -0.45 to -0.06; p = 0.01), and combined age and PSA (OR = -0.26; 95% CI: -0.47 to -0.06; p = 0.01). The association persisted after adjustments for inflammatory markers such as MLR (OR = -0.22; 95% CI: -0.40 to -0.04; p = 0.02) and SIRI (OR = -0.24; 95% CI: -0.42 to -0.06; p = 0.01). Urinary Copper and PC Urinary copper concentrations showed weak trends toward significance across all adjusted models (Table S4). Adjustments for age (OR = 0.04; 95% CI: 0.00–0.08; p = 0.07), PSA (OR = 0.03; 95% CI: -0.01–0.07; p = 0.11), and combined age and PSA (OR = 0.03; 95% CI: -0.01–0.07; p = 0.17) did not yield significant associations. Urinary Lead and PC Urinary lead concentrations remained significantly associated with PC after adjustment for demographic factors (Table S4). Adjustment for age yielded an OR of 0.31 (95% CI: 0.04–0.57; p = 0.03), while PSA adjustment resulted in an OR of 0.31 (95% CI: 0.03–0.58; p = 0.03). Combined adjustment for age and PSA strengthened the association (OR = 0.33; 95% CI: 0.04–0.61; p = 0.02). Borderline significance was observed for adjustments involving hematological parameters and inflammatory indices, with p-values ranging from 0.06 to 0.08. Table 1 Table 1 Demographics and CBC parameters for BPH, PL, and PC subjects. PSA = prostate specific antigen; WBC = white blood cells; RBC = red blood cells: HGB = haemoglobin; LUC = large unidentified cells; PLT = platelets; MPV = mean platelet volume; PCT = plateletcrit; MPR = mean platelet volume to platelet count ratio; NLR = neutrophils to lymphocytes ratio; MLR = monocytes to lymphocytes ratio; SIRI = systemic inflammation response index. Variable BPH (n = 61) PL (n = 15) PC (n = 78) p Age 68 (64–75) 69 (65–72) 74 (69–77) 0.002 Total PSA (ng/ml) 5.9 (3.3–9.4) 4.4 (1.2–6) 7.5 (5.4–14) 0.0002 WBC (10 3 /µL) 7.1 ± 1.7 7.5 ± 2.7 7.1 ± 1.9 0.95 RBC (10 6 /µL) 5.2 ± 0.63 5.3 ± 0.77 5 ± 0.7 0.29 HGB (g/dl) 15 (14–16) 14 (14–15) 14 (13–15) 0.019 Neutrophils (10 3 /µL) 4.2 ± 1.3 4.4 ± 1.9 4.3 ± 1.5 0.87 Lymphocytes (10 3 /µL) 2.1 (1.6–2.7) 1.9 (1.6–2.3) 1.9 (1.5–2.3) 0.47 Monocytes (10 3 /µL) 0.5 (0.4–0.5) 0.5 (0.4–0.6) 0.4 (0.4–0.5) 0.31 LUC counts (10 3 /µL) 0.1 (0.1–0.2) 0.1 (0.1–0.2) 0.1 (0.1–0.2) 0.17 PLT (10 3 /µL) 225 ± 52 218 ± 50 218 ± 60 0.78 MPV (fL) 8.8 ± 0.71 8.8 ± 0.83 8.8 (8.2–9.2) 0.99 PCT (%) 1.9 (1.6–2.3) 2 ± 0.48 1.9 ± 0.53 0.89 MPR 0.04 (0.03–0.05) 0.042 ± 0.011 0.04 (0.03–0.05) 0.98 NLR 2 (1.6–2.5) 2.4 ± 1.1 2.3 ± 0.85 0.38 MLR 0.22 (0.18–0.28) 0.3 ± 0.1 0.25 ± 0.079 0.03 SIRI 0.94 (0.68–1.1) 1.1 (0.83–1.8) 0.92 (0.75–1.4) 0.27 Table 2 Table 2 Blood and urine heavy metal concentrations (unit). BPH = benign prostatic hyperplasia; PL = pre-cancerous lesion; PC = prostate cancer; KW = Kruskal-Wallis test. Patient Plasma (µg/L) KW test Urine (µg/L) KW test T or U test (plasma Vs. urine) Manganese BPH 3.1 (1.7–3.5) 0.049 3 (2–4.3) 0.919 0.26 PL 2.4 ± 0.97 3 ± 1 0.143 PC 3.3 (2–3.8) 3.1 ± 1.3 0.845 Cobalt BPH 0.24 (0.23–0.26) 0.522 0.27 (0.19–0.46) 0.971 0.163 PL 0.25 ± 0.051 0.34 ± 0.17 0.07 PC 0.25 (0.23–0.28) 0.29 (0.2–0.46) 0.502 Vanadium BPH 0.047 ± 0.024 0.179 0.06 (0.04–0.11) 0.971 0.041 PL 0.072 ± 0.054 0.06 (0.04–0.12) 0.979 PC 0.06 (0.04–0.07) 0.06 (0.04–0.098) 0.826 Iron BPH 1227 (880–1631) 0.519 20 (12–33) 0.991 < 0.0001 PL 1095 ± 387 25 ± 20 < 0.0001 PC 1236 (812–1615) 19 (13–32) < 0.0001 Copper BPH 839 (762–911) 0.88 10 (7.3–13) 0.398 < 0.0001 PL 843 ± 121 12 ± 4.7 < 0.0001 PC 862 (730–988) 11 (8.8–16) < 0.0001 Zinc BPH 1695 (1325–2665) 0.384 518 (357–695) 0.84 < 0.0001 PL 2141 ± 790 511 ± 280 < 0.0001 PC 1639 (1286–2444) 508 ± 260 < 0.0001 Selenium BPH 112 ± 26 0.524 30 (19–44) 0.811 < 0.0001 PL 105 ± 28 32 ± 21 < 0.0001 PC 115 ± 30 32 (22–42) < 0.0001 Arsenic BPH 1.9 (0.54–3.8) 0.697 48 (17–143) 0.211 < 0.0001 PL 0.93 (0.4–3.3) 40 (17–151) < 0.0001 PC 0.99 (0.52–3.6) 22 (9.3–102) < 0.0001 Molybdenum BPH 0.94 (0.7–1.2) 0.423 42 (27–78) 0.383 < 0.0001 PL 0.91 (0.63–1.2) 32 (21–57) < 0.0001 PC 0.98 (0.76–1.4) 46 (27–64) < 0.0001 Cadmium BPH 0.01 (0.01–0.02) 0.506 0.41 (0.24–0.61) 0.835 < 0.0001 PL 0.01 (0.01–0.025) 0.42 ± 0.29 < 0.0001 PC 0.01 (0.01–0.02) 0.39 (0.27–0.57) < 0.0001 Antimony BPH 7.8 ± 1.4 0.034 0.04 (0.03–0.078) 0.9 < 0.0001 PL 7.4 ± 1.8 0.045 (0.03–0.1) < 0.0001 PC 7.1 ± 1.3 0.04 (0.03–0.06) < 0.0001 Barium BPH 1.1 (0.8–1.7) 0.398 1.3 (0.68–2.7) 0.443 0.019 PL 1.4 ± 0.54 1.9 (1.2–4) 0.099 PC 1 (0.77–1.6) 1.5 (0.77–2.6) 0.055 Mercury BPH 0.64 (0.43–1) 0.75 1 (0.33–2) 0.579 0.264 PL 0.52 (0.32–1.1) 0.93 (0.49–1.6) 0.227 PC 0.65 (0.36–1.1) 0.72 (0.37–1.3) 0.36 Lead BPH 1.1 (0.22–1.5) 0.871 0.97 (0.63–2) 0.263 0.18 PL 0.82 (0.17–1.8) 1.4 ± 0.63 0.303 PC 1.1 (0.2–2.2) 1.4 (0.83–2) 0.235 Table 3 Table 3 Unadjusted individual metal probit regression models for the association of heavy metal concentrations and PL. OR = odds ratio. BPH Vs. PL Unadjusted OR 95% CI P Manganese Plasma -0.24 -0.59 0.11 0.18 Urine -0.09 -0.35 0.18 0.52 Cobalt Plasma -1.4 -6.49 3.69 0.59 Urine -0.42 -1.54 0.69 0.46 Vanadium Plasma 11.67 0.72 22.63 0.04 Urine -0.79 -3.42 1.84 0.56 Iron Plasma 0 0 0 0.24 Urine 0 -0.03 0.03 0.96 Copper Plasma 0 0 0 0.52 Urine 0.01 -0.06 0.07 0.8 Zinc Plasma 0 0 0 0.43 Urine 0 0 0 0.91 Selenium Plasma -0.01 -0.02 0.01 0.44 Urine 0 -0.02 0.02 0.99 Arsenic Plasma -0.02 -0.09 0.06 0.68 Urine 0 0 0 0.25 Molybdenum Plasma -0.05 -0.38 0.27 0.76 Urine 0 -0.02 0 0.17 Cadmium Plasma 21.9 -14.72 58.54 0.24 Urine -0.24 -1.32 0.85 0.67 Antimony Plasma -0.1 -0.33 0.13 0.39 Urine 0.25 -0.83 1.33 0.65 Barium Plasma 0.25 -0.34 0.85 0.41 Urine 0.12 -0.07 0.31 0.23 Mercury Plasma -0.2 -0.66 0.26 0.4 Urine 0.07 -0.32 0.19 0.62 Lead Plasma -0.05 -0.3 0.2 0.7 Urine 0.09 -0.35 0.52 0.7 Table 4 Table 4 Unadjusted individual metal probit regression models for the association of heavy metal concentrations and PC. OR = odds ratio. BPH Vs. PC Unadjusted OR 95% CI P Manganese Plasma 0.07 -0.12 0.26 0.48 Urine -0.05 -0.21 0.12 0.58 Cobalt Plasma 0.00 -2.42 2.43 1.00 Urine -0.39 -1.05 0.27 0.25 Vanadium Plasma 7.92 -1.04 16.87 0.08 Urine -0.48 -1.84 0.88 0.49 Iron Plasma 0.00 0.00 0.00 0.50 Urine 0.00 -0.01 0.02 0.43 Copper Plasma 0.00 0.00 0.00 0.92 Urine 0.03 0.00 0.07 0.06 Zinc Plasma 0.00 0.00 0.00 0.56 Urine 0.00 0.00 0.00 0.78 Selenium Plasma 0.00 -0.01 0.01 0.64 Urine 0.00 0.00 0.01 0.57 Arsenic Plasma 0.00 -0.02 0.03 0.83 Urine 0.00 0.00 0.00 0.80 Molybdenum Plasma 0.00 -0.21 0.20 0.98 Urine 0.00 -0.01 0.00 0.26 Cadmium Plasma 5.31 -19.28 29.89 0.67 Urine 0.10 -0.55 0.74 0.77 Antimony Plasma -0.25 -0.43 -0.08 0.00 Urine -1.95 -4.59 0.70 0.15 Barium Plasma -0.05 -0.41 0.32 0.80 Urine 0.05 -0.06 0.17 0.36 Mercury Plasma -0.10 -0.35 0.15 0.44 Urine -0.12 -0.29 0.04 0.15 Lead Plasma 0.04 -0.10 0.17 0.60 Urine 0.28 0.02 0.54 0.03 Comparative Analysis of Metal Concentrations in Prostate Disorders: Insights into Biomarkers and Disease Progression The statistical analysis of metal concentrations in benign prostatic hyperplasia, precancerous lesions, and prostate cancer groups was conducted using the Mann-Whitney U test, a non-parametric method appropriate for comparing independent groups where data may not follow a normal distribution. The test allowed pairwise comparisons between BPH vs. PL, BPH vs. PC, and PL vs. PC, identifying statistically significant differences in metal concentrations across the groups. The box plots illustrate the distribution of these metal concentrations in serum, Fig. 3 , with medians, interquartile ranges (IQR), and whiskers representing the full range of data. A gray zone indicating the mean ± standard deviation (SD) for each group further facilitated comparison across the clinical conditions. The results revealed that plasma manganese concentrations showed a marginally significant difference (p = 0.049) in the PC group compared to BPH and PL, suggesting a potential, but inconclusive role in prostate cancer progression. No significant differences were observed in urinary manganese concentrations (p = 0.919). In contrast, antimony levels were significantly lower in the PC group, aligning with previous findings that associate reduced antimony concentrations with prostate cancer risk. Elevated vanadium levels in the PL group indicate its potential role in precancerous processes, possibly serving as a diagnostic biomarker for early disease stages. For urine samples, manganese concentrations were consistent across groups, while vanadium and copper concentrations were elevated in the PL group. Furthermore, lead concentrations were significantly higher in the PC group, suggesting a link between lead exposure and prostate cancer risk. Importantly, the reference values used for comparison were taken from Tables 11 and 12 of the ISTISAN Report 17/33, which provides diagnostic reference levels for heavy metals in biological samples. These diagnostic reference levels offer a benchmark for evaluating the observed metal concentrations within the context of prostate health and disease. The results underscore the potential of metals such as vanadium and lead as biomarkers for prostate disease progression and highlight the value of using established diagnostic reference levels in interpreting clinical biochemistry data, Fig. 4 . Discussion The results of this study provide valuable insights into the relationship between heavy metal concentrations in plasma and urine and key components of the clinical continuum linking benign states with precancerous and cancerous prostate pathologies. This continuum was further supported by the observed differences in median age and total PSA concentrations across the three groups. Additionally, the reduction in haemoglobin concentrations and the increase in the monocyte-to-lymphocyte ratio (MLR) in PL and PC patients can be interpreted as indicators of potential immunological alterations in these groups, as well as anaemia associated with preneoplastic or neoplastic states. The MLR is a measure of the balance between the body's innate immune response (monocytes) and adaptive immune response (lymphocytes). An increased MLR may indicate, in the context of cancer, a shift from adaptive immunity (which relies on specific immune responses to pathogens) to innate immunity (which provides rapid and nonspecific defence against pathogens) [ 36 ]. Additionally, a high MLR may also reflect an ongoing inflammatory response, which is common in cancer and precancerous conditions [ 37 ]. While these changes provide insights into the body's defense mechanisms, they also highlight the complexity of the immune response in cancer patients. The evaluation of heavy metal concentrations revealed significant differences for antimony, with lower plasma levels associated with PC, while manganese showed marginally significant differences in plasma across groups, suggesting potential roles of these metals in the pathophysiology of prostate diseases. According to the International Agency for Research on Cancer (IARC), antimony trioxide (Sb 2 O 3 ) has been classified as a possible carcinogenic to humans (Group 2B), while antimony trisulfide (Sb 2 S 3 ) has not been classified with respect to its carcinogenicity (Group 3) [ 38 ]. There are indications of neoplastic or preneoplastic effects of antimony species in animal models across various tissues and organs. Two recent studies suggested that antimony acts as an endocrine disruptor through interactions with oestrogen and androgen receptors, potentially promoting tumour growth in the reproductive system, although its relevance for in vivo carcinogenesis in rodents and humans remains unclear [ 39 , 40 ]. The observed marginal significance for plasma manganese levels in PC aligns with its known roles in cellular metabolism and oxidative stress. However, further studies are needed to confirm its association with prostate cancer risk and its potential as a biomarker. The absence of significant differences in urinary heavy metal concentrations across the groups suggests that the potential impact of heavy metals on prostate health and disease states is more closely related to systemic exposure rather than renal excretion patterns [ 41 ]. Regression analysis provided additional evidence for the association between heavy metal concentrations and the presence of PL and PC. Plasma vanadium concentrations were significantly higher in the PL group, with a similar but non-significant trend observed in PC patients. This finding suggests that vanadium may play a role in the early stages of prostate neoplasia. In the literature, the pro-tumor effect of vanadium refers to the potential adverse effects of vanadium exposure on tumor progression and/or the promotion of cancerous conditions [ 42 , 43 ]. Although vanadium has been studied for its anti-tumor properties, the lack of a significant association with PC in this study indicates that its role might be more relevant in precancerous states [ 44 ]. The analysis also revealed a trend toward a significant association between urinary copper concentration and PC. Variations in copper concentrations or copper/zinc ratios (Cu/Zn) are associated with several tumors, including those of the bladder, breast, colon-rectum, and prostate [ 45 ]. However, in this study, copper showed only weak trends toward significance, suggesting its limited utility as a biomarker in the current cohort. Another interesting finding concerns the significant association between urinary lead concentration and PC. Previous studies have suggested a potential link between environmental lead exposure and the risk of developing prostate diseases [ 46 ]. Higher blood lead concentrations have been reported in PC cases, suggesting that environmental lead exposure may influence prostate pathology risk. The consistent association of urinary lead with PC, even after adjustment for confounders, highlights its potential as a robust biomarker for prostate cancer risk [ 47 , 48 ]. Adjusting for potential confounding factors such as age, total PSA, and complete blood count (CBC) did not alter the significant association between higher plasma vanadium concentrations and PL, reinforcing the possible role of vanadium in prostate health. However, the trend toward an association between copper concentration and PC did not persist after adjustment, suggesting that its role might be secondary or confounded by other variables [ 49 , 50 ]. Globally, these results underscore the importance of exploring plasma antimony and vanadium as potential biomarkers for prostate pathologies, while highlighting the need for further research to confirm the roles of manganese, copper, and lead in prostate cancer progression [ 51 , 52 ]. A final remark should be dedicated to the comparison of our results to those present in the literature for the same type of cancer [ 23 , 53 ]. The heterogeneity in the type of biological samples used for the determination of metal concentrations (serum, tissue, hair and nails, blood and urine) and in the patients enrolled, unfortunately, does not allow us any sound comparison and conclusion. We can only report that in general the metals showing altered concentrations in the case of PC are mainly zinc, selenium, arsenic, cadmium, iron, lead, and to a lesser extent copper, manganese, mercury and antimony. Especially zinc seems to have a protective role against development of PC, as confirmed in different studies where PC patients show low levels of this essential metal. In ours, zinc does not correlate to any prostate pathology. Instead, we have found strong evidence that vanadium is, while the involvement of this element was never reported before. This indicates that further studies are needed to clarify such correlations, together with the need of a common and shared methodology for a better comparison of the results. Materials and Methods Male subjects with prostate cancer, precancerous lesions, or benign prostatic hypertrophy were recruited from the Urology Department of the University Hospital of Sassari. A total of 78 cases (PC and PL) and 76 controls (BPH) were initially recruited, meeting the calculated sample size required for sufficient statistical power. The power calculation was based on an expected mean difference (ΔΔ) of 5 µg/Lµg/L (difference in metal levels between cases and controls) and a standard deviation (σσ) of 10 µg/Lµg/L, as derived from preliminary data and literature. Using these parameters, the study achieved > 80% power to detect a medium effect size (Cohen's d = 0.5) at a significance level of α = 0.05/3, adjusted for Bonferroni correction for the three tested metals. However, due to incomplete data or missing measurements for some metals, the final analysis included data from 61 controls (BPH). Despite this limitation, the available data ensured sufficient power (> 75%) to detect the expected effect size, supporting the validity of this exploratory biomarker study. The study was conducted in accordance with the Declaration of Helsinki and was approved by the Independent Ethics Committee of the University Hospital of Cagliari (P.O. San Giovanni di Dio: via Ospedale 54–09124 Cagliari) under Approval Code Prot. PG/2022/4985, dated March 30, 2022. Written informed consent was obtained from all participants prior to their inclusion in the study. Criteria Exclusion from the study was based on factors that may influence metal concentration: a previous diagnosis of PC, other cancers, or a subsequent diagnosis of metastatic PC after biopsy. Patients were included if they had a positive screening test (abnormal Digital Rectal Examination (DRE), high age-specific serum Prostate-Specific Antigen (PSA) concentration, or serum PSA of > 0.35 ng/mL per year). A questionnaire capturing demographic information, medical history, family history of cancer, PC screening, urological health, and lifestyle factors (e.g., smoking and physical activity) was used in each participant. Ultrasound biopsy was performed to determine the following groups: i) BPH; ii) high-grade prostatic intraepithelial neoplasia or prostatic atypical small acinar proliferation/atypia (PL); and iii) PC. Heavy Metals Serum Analysis Using ICP-MS Total metals and non-metals in plasma (aluminium, silver, beryllium, chromium, lithium, nickel, tin, thallium, manganese, cobalt, vanadium, iron, copper, zinc, selenium, arsenic, molybdenum, cadmium, antimony, barium, mercury and lead) and urine (antimony, arsenic, cadmium, manganese, molybdenum, lead, copper, selenium, tin, thallium, zinc) were determined by inductively coupled plasma mass spectrometry (ICP-MS) in accordance with the US EPA 6020B method [ 54 ]. Biological fluids were analysed directly after dilution of 0.5 mL of sample in 5 mL with 2% nitric acid (J.T. Baker, Phillipsburg, NJ, USA) solution. The analysis was performed with an inductively coupled plasma mass spectrometer ICP-MS/MS (Agilent 8800 QQQ, Santa Clara, CA, USA) equipped with a collision cell and two quadrupole mass analyzers. In comparison to a single quadrupole ICP-MS system, the triple quadrupole system significantly increases the accuracy of mass separation. To compensate for the matrix effect and signal drift, a solution of internal standards was used. The calibration curve was verified at the start of each analytical batch using the initial calibration verification (ICV) with a different lot standard, while the instrumental sensitivity was verified using the continuous calibration verification (CCV) at or near midrange. The LOQs testing was 0.001 ng/mL for all elements analysed. The quality control of the data was verified and controlled using Certified Reference Materials ClinChek® Urine Control, and ClinChek® Plasma Control for Trace Elements, (RECIPE Chemicals, München). Laboratory was intercalibrated through successful participation in internationally organized proficiency tests (OELM). The method is accredited according to UNI EN ISO 17025/2017 [ 55 , 56 ]. Statistical Analysis Differences in subject characteristics, including age, ethnicity, education, marital status, family cancer history, were compared between PC cases and BPH or PL using the chi-squared test. To ensure that there was no selection bias when samples for the assay were chosen, the chi-squared test was used to compare background characteristics of the corresponding subjects between groups. The Shapiro-Will test of normality was used to test the statistical distribution of each variable. Data are presented as mean and standard deviation (SD) or median and interquartile range (IQR), and T or U test were used accordingly to test for the presence of any difference between two parametric or non-parametric distributions. The Kruskall-Wallis test was used to assess differences of more than two non-parametric distributed variables. Probit regression models to estimate odds ratio (OR) with 95% confidence intervals (CI) for univariate linear regression analysis of each heavy metal concentration in plasma and urine, and its association with pre-cancerous lesion or prostate cancer. The models were also adjusted in bivariate linear regression analyses for age, total PSA, and the following haematological parameters: WBC, RBC, HBG, neutrophils, lymphocytes, monocytes, and LUC). Statistical analyses were performed using Stata 14 (STATA Corp., College Station, TX, USA). The R heatmap package was used to create the heatmaps. The inclusion of haematological parameters (WBC, RBC, HBG, neutrophils, lymphocytes, monocytes, and LUC) as confounders in the regression models was driven by their established relevance in the pathophysiology of prostate diseases. Chronic inflammation plays a central role in prostate carcinogenesis, and WBC levels, as a marker of systemic inflammation, can influence tumour progression and the tumour microenvironment. Similarly, RBC and HBG are indicative of anaemia or disruptions in erythropoiesis, which are frequently observed in cancer patients and can affect the systemic transport and bioavailability of heavy metals. Parameters such as neutrophils and lymphocytes, key components of the immune response, are closely tied to the inflammatory milieu and immune modulation in cancer. By adjusting for these variables, the analysis aimed to reduce confounding effects and isolate the specific associations between heavy metal concentrations and prostate pathology. These adjustments improve the robustness of the findings by accounting for potential interactions between systemic inflammation, haematological status, and metal bioaccumulation. Conclusions This study provides compelling evidence of the complex interaction between heavy metal concentrations and the risk of developing prostate diseases. The significant associations between plasma vanadium concentrations and the risk of PL and PC, as well as the potential protective role of plasma antimony against PC, highlight the need for further research to clarify the underlying mechanisms and explore the potential of heavy metals as biomarkers or therapeutic targets for prostate health. The results also emphasize the importance of considering systemic exposure to heavy metals in the context of prevention and management strategies for prostate diseases. Declarations Funding: This project and D.C. were supported by the Autonomous Region of Sardinia, pursuant to Regional Law August 7, 2007, No. 7 "Promotion of Scientific Research and Technological Innovation in Sardinia - Project UGOV RAS_CRP2023 CARRU: “Role of Circulating Biomarkers in the Management of Patients Affected by Prostate Carcinoma”. National LILT Program - 5 per thousand years 2022, Scientific-Sanitary Research Call LILT 2023: “Study of the Heterogeneity of Prostate Carcinoma as a Key for Dissecting Cellular Subtypes That Characterize It”, Number: LILT - Protocol Number 2024U0001294 dated March 29, 2024. Se.M. was funded by Fondazione di Sardegna, MEDICI 2017 Project "Nuclear Magnetic Resonance Studies: Toxic Metals and Carcinogens and Their Interactions with Cellular Targets" and MEDICI 2024 Project “Metalli Pesanti e Tumori: Comprendere l'Impatto Ambientale sulla Salute Umana”. Institutional Review Board Statement: The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee COMITATO ETICO INDIPENDENTE - Azienda Ospedaliero Universitaria di Cagliari P.O. San Giovanni di Dio: via Ospedale 54 – 09124 Cagliari. Approval Code: Prot. PG/2022/4985. Ap-proval Date: 30/03/2022. Informed Consent Statement: Written informed consent has been obtained from the patients to publish this paper. Data availability statement: The datasets generated and analyzed during this study are available from the corresponding author upon reasonable request. Researchers seeking access may be required to provide a brief proposal outlining the intended use and agree to confidentiality terms as per institutional policies. Additional information: The authors declare no competing financial, professional, or personal interests that could influence the performance or presentation of this work. References Rawla, P. Epidemiology of prostate cancer. 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Supplementary Files SupplementarymaterialsHeavyMetalsandProstateCancer.pdf Cite Share Download PDF Status: Published Journal Publication published 24 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 13 Feb, 2025 Reviews received at journal 10 Feb, 2025 Reviews received at journal 29 Jan, 2025 Reviewers agreed at journal 29 Jan, 2025 Reviewers agreed at journal 18 Jan, 2025 Reviewers invited by journal 17 Jan, 2025 Editor assigned by journal 17 Jan, 2025 Editor invited by journal 14 Jan, 2025 Submission checks completed at journal 14 Jan, 2025 First submitted to journal 13 Jan, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Each row represents an individual subject (grouped as BPH, PL, and PC, and annotated in green, yellow, and red, respectively) and heavy metal concentration values are scaled from -10 to 10 (blue to red). The data matrix uses a range of colors to represent values, with darker or more intense colors indicating higher values and lighter or less intense colors indicating lower values.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-5822110/v1/bbb62134ea169c789078b9a2.png"},{"id":73960136,"identity":"c034bfe0-3973-4d51-ac71-f9f290f6195e","added_by":"auto","created_at":"2025-01-16 11:25:09","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":175511,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of\u003cstrong\u003e \u003c/strong\u003eurinary heavy metal concentrations. Each row represents an individual subject (grouped as BPH, PL, and PC, and annotated in green, yellow, and red, respectively) and heavy metal concentration values are scaled from -10 to 10 (blue to red). The data matrix uses a range of colors to represent values, with darker or more intense colors indicating higher values and lighter or less intense colors indicating lower values.\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5822110/v1/b83664ff27c3a0390125b056.jpeg"},{"id":73960135,"identity":"431ab30a-d525-4337-9849-15eb87b51c20","added_by":"auto","created_at":"2025-01-16 11:25:09","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":76037,"visible":true,"origin":"","legend":"\u003cp\u003eStatistical analysis of metal concentrations in serum in Benign Prostatic Hyperplasia (BPH), Precancerous Lesions (PL), and Prostate Cancer (PC).\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5822110/v1/96af8623e4f06a9360bf49cb.jpeg"},{"id":73959533,"identity":"66c52b1d-6d48-4f74-8761-7ea3a1933c19","added_by":"auto","created_at":"2025-01-16 11:17:09","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":79618,"visible":true,"origin":"","legend":"\u003cp\u003eStatistical analysis of metal concentrations in Urine in Benign Prostatic Hyperplasia (BPH), Precancerous Lesions (PL), and Prostate Cancer (PC).\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5822110/v1/375028507157061b73b3f712.jpeg"},{"id":81570851,"identity":"9c7ab7c8-3cb2-4446-bef1-790d290e1fa0","added_by":"auto","created_at":"2025-04-28 16:14:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1921876,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5822110/v1/4e480361-cf0a-4b90-9b33-a727564eb009.pdf"},{"id":73959537,"identity":"1680b2ca-d7b7-4e2b-84cd-ced5e734cd21","added_by":"auto","created_at":"2025-01-16 11:17:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":285350,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialsHeavyMetalsandProstateCancer.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5822110/v1/689e16f8b6e6d6dd5b36fb6b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Heavy metals and prostate cancer: a new study with new findings","fulltext":[{"header":"Introduction","content":"\u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eProstate cancer (PC) is the second most common cancer in men, with over a million cases diagnosed globally in 2018, representing 15% of all cancers [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The Lancet Commission has recently published projections indicating a significant increase in PC cases from 1.4\u0026nbsp;million in 2020 to 2.9\u0026nbsp;million by 2040, particularly in low and middle-income countries [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This increase cannot be prevented with lifestyle changes or public health interventions only, highlighting the need for alternative strategies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Commission emphasizes the importance of improving early diagnosis, developing more effective treatments, and implementing educational programs that shift attention from the treatment of advanced disease to that of the early stage, underscoring the urgency of timely intervention. It is also essential to intensify research to better understand PC causes and development [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe burden of PC is mainly attributed to several factors, including family history, hormones, ethnicity, aging, and inflammatory states [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The observed incidence increase is also associated with transitioning from traditional diets rich in fibres and carbohydrates to Westernized diets characterized by a higher concentration of processed red meat and saturated fats [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Such transitioning is influenced by economic growth, contributing to reduced physical activity and increased prevalence of overweight and obesity [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. According to the World Cancer Research Fund and the American Cancer Society, up to 16% of PC cases could be prevented by eliminating unhealthy diets and reducing physical inactivity and obesity, while keeping other risk factors unchanged [\u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Most PC cases are diagnosed between the ages of 50 and 79. Given the increasing life expectancy, the burden of PC has become a health priority [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs previously mentioned, chronic inflammation plays a critical role in the development of PC. Exposure to heavy metals may favour chronic inflammation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Heavy metals are not univocally defined, as their classification can be also based on their physio-chemical properties [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Broadly speaking, they include the d- and f-block transition elements, together with p-block metals and some non-metals like selenium and arsenic [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Their biological role can vary from essential to toxic. Essential trace elements, like iron, copper, cobalt, manganese, zinc, chromium, vanadium, nickel and molybdenum are crucial for enzyme activity, immune regulation, protection from oxidative damage, genomic stability, apoptosis, and cell signalling. Other metals such as cadmium [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], mercury and lead are toxic [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] (included in Group 1 substances carcinogenic to humans, or suspected to be). However, even essential metals can be toxic if present in high concentrations [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These elements can accumulate in the body through exposure to industrial, environmental, or food substances [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The accumulation of heavy metals can lead to oxidative damage, which has been associated with a range of diseases, including cancer [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBesides inflammation and oxidative damage, epidemiological studies have shown that exposure to heavy metals can influence the endocrine system, which regulates growth, development, metabolism, and reproduction [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This can impact the progression of cancer, including PC. Literature reports a number of studies carried out to identify and quantify heavy elements in biological samples of cancer patients (blood, urine, hair, nail, biopsies, etc.) in order to clarify the relationship between the presence of these pollutants in the organism and the risk of developing the disease. We have recently examined these studies and reunited them in a review [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] that shows how the results obtained are not always univocal, as the possibility of sound conclusions is limited by the heterogeneity of the data collected in terms of biological samples examined, methodologies, and cohorts of patients selected. Therefore, we decided to add further evidence to these findings by carrying out our own research on this topic.\u003c/p\u003e \u003cp\u003eOur study sought to quantify the concentrations of different metals in blood and urine samples of individuals undergoing diagnostic assessment for prostate pathology. Specifically, we investigated associations between such concentrations and different types of prostate pathology, including PC [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This research, conducted with patients from the Urology Clinic at the University Hospital of Sassari (Italy), aims to provide critical insights into the potential role of heavy metal exposure in the aetiology and progression of prostate disorders, thereby advancing our understanding of its implications for prostate health and disease management.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eSubject characteristics\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eFrom September 2021 to December 2022, a total of 156 participants, all from Sardinia (Italy), were enrolled. The cohort included 61 subjects with BPH, 15 with PL, and 78 with confirmed PC. Age and total PSA, according to the guidelines [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e], were significantly different across the three groups, with PC patients being older (BPH median age\u0026thinsp;=\u0026thinsp;68; PL\u0026thinsp;=\u0026thinsp;69; PC\u0026thinsp;=\u0026thinsp;74; p\u0026thinsp;=\u0026thinsp;0.002) and having higher total PSA concentrations than the other two groups (BPH median total PSA\u0026thinsp;=\u0026thinsp;5.9 ng/mL; PL\u0026thinsp;=\u0026thinsp;4.4 ng/mL; PC\u0026thinsp;=\u0026thinsp;7.5 ng/mL; p\u0026thinsp;=\u0026thinsp;0.0002). Hemoglobin concentrations were also significantly lower in PL and PC patients (BPH median RBC\u0026thinsp;=\u0026thinsp;15 g/dl; PL\u0026thinsp;=\u0026thinsp;14 g/dl; PC\u0026thinsp;=\u0026thinsp;14 g/dl; p\u0026thinsp;=\u0026thinsp;0.019), while other CBC parameters did not differ across the study groups. Among the CBC inflammatory indices, only the monocytes-to-lymphocytes ratio (MLR) was increased in PL and PC patients (BPH mean MLR\u0026thinsp;=\u0026thinsp;0.22; PL mean MLR\u0026thinsp;=\u0026thinsp;0.3; PC mean MLR\u0026thinsp;=\u0026thinsp;0.25; Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eStatistically significant differences were observed in plasma concentrations of manganese and antimony across the three groups (p\u0026thinsp;=\u0026thinsp;0.049 and 0.034, respectively), while no between-group differences were observed in urinary concentrations of any heavy metal. These findings were also illustrated in heat maps depicting heavy metal concentrations in plasma and urine (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eAdditionally, urinary iron, copper, zinc, selenium, arsenic, molybdenum, cadmium, and antimony concentrations were significantly lower than plasma concentrations in all three groups. By contrast, barium urinary concentrations were significantly lower than plasmatic ones only in the BPH group, as reflected in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. No significant differences were observed for manganese concentrations between plasma and urine across the groups. (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted Probit Regression Analysis of Metal Concentrations in Plasma and Urine and Their Associations with Prostate Pathologies\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eProbit regression analyses were conducted to evaluate the odds ratio (OR) and 95% confidence interval (CI) for the association between plasma or urinary concentrations of individual heavy metals and the presence of precancerous lesions or prostate cancer, with the benign prostatic hyperplasia group serving as a control (Tables \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eElevated plasma vanadium concentrations were significantly associated with PL (OR\u0026thinsp;=\u0026thinsp;11.67; 95% CI: 0.72\u0026ndash;22.63; p\u0026thinsp;=\u0026thinsp;0.04), with a non-significant trend for a similar association in PC patients (OR\u0026thinsp;=\u0026thinsp;7.92; 95% CI: -1.04\u0026ndash;16.87; p\u0026thinsp;=\u0026thinsp;0.08). In contrast, lower plasma antimony concentrations were significantly associated with PC (OR = -0.25; 95% CI: -0.43 to -0.08; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant associations were observed between urinary heavy metal concentrations and PL, but there was a trend for an association between urinary copper and PC (OR\u0026thinsp;=\u0026thinsp;0.03; 95% CI: 0.00\u0026ndash;0.07; p\u0026thinsp;=\u0026thinsp;0.06) and a significant association for urinary lead and PC (OR\u0026thinsp;=\u0026thinsp;0.28; 95% CI: 0.02\u0026ndash;0.54; p\u0026thinsp;=\u0026thinsp;0.03).\u003c/p\u003e\n \u003cp\u003eSignificant or trend-level associations observed in single-metal regression models were further evaluated in bivariate probit regression analyses adjusted for potential confounders, including age, total PSA, and CBC parameters.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003ePlasma Vanadium and PL\u003c/h3\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eAfter adjustment, elevated plasma vanadium concentrations remained significantly associated with PL when corrected for RBC (OR\u0026thinsp;=\u0026thinsp;13.10; 95% CI: 1.39\u0026ndash;24.81; p\u0026thinsp;=\u0026thinsp;0.03), HGB (OR\u0026thinsp;=\u0026thinsp;11.93; 95% CI: 0.69\u0026ndash;23.17; p\u0026thinsp;=\u0026thinsp;0.04), lymphocytes (OR\u0026thinsp;=\u0026thinsp;11.87; 95% CI: 0.58\u0026ndash;23.16; p\u0026thinsp;=\u0026thinsp;0.04), and age (OR\u0026thinsp;=\u0026thinsp;12.35; 95% CI: 0.99\u0026ndash;23.70; p\u0026thinsp;=\u0026thinsp;0.03; Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). No significant associations were observed after adjustments for systemic inflammatory indices such as MLR or SIRI (p\u0026thinsp;\u0026gt;\u0026thinsp;0.10). These results suggest a robust relationship between plasma vanadium and PL.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003ePlasma Vanadium and PC\u003c/h3\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eIn contrast, no significant associations were observed between plasma vanadium and PC after adjustment for confounders (Table S2). Adjustment for age (OR\u0026thinsp;=\u0026thinsp;5.40; 95% CI: -3.85\u0026ndash;14.65; p\u0026thinsp;=\u0026thinsp;0.25), PSA (OR\u0026thinsp;=\u0026thinsp;6.96; 95% CI: -3.27\u0026ndash;17.19; p\u0026thinsp;=\u0026thinsp;0.18), and hematological parameters such as RBC (OR\u0026thinsp;=\u0026thinsp;7.75; 95% CI: -2.05\u0026ndash;17.54; p\u0026thinsp;=\u0026thinsp;0.12) did not yield significant results.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003ePlasma Antimony and PC\u003c/h3\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003ePlasma antimony concentrations were consistently and significantly associated with PC across all adjusted models (Table S3). The association remained significant after adjustment for age (OR = -0.28; 95% CI: -0.46 to -0.09; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PSA (OR = -0.26; 95% CI: -0.45 to -0.06; p\u0026thinsp;=\u0026thinsp;0.01), and combined age and PSA (OR = -0.26; 95% CI: -0.47 to -0.06; p\u0026thinsp;=\u0026thinsp;0.01). The association persisted after adjustments for inflammatory markers such as MLR (OR = -0.22; 95% CI: -0.40 to -0.04; p\u0026thinsp;=\u0026thinsp;0.02) and SIRI (OR = -0.24; 95% CI: -0.42 to -0.06; p\u0026thinsp;=\u0026thinsp;0.01).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eUrinary Copper and PC\u003c/h3\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eUrinary copper concentrations showed weak trends toward significance across all adjusted models (Table S4). Adjustments for age (OR\u0026thinsp;=\u0026thinsp;0.04; 95% CI: 0.00\u0026ndash;0.08; p\u0026thinsp;=\u0026thinsp;0.07), PSA (OR\u0026thinsp;=\u0026thinsp;0.03; 95% CI: -0.01\u0026ndash;0.07; p\u0026thinsp;=\u0026thinsp;0.11), and combined age and PSA (OR\u0026thinsp;=\u0026thinsp;0.03; 95% CI: -0.01\u0026ndash;0.07; p\u0026thinsp;=\u0026thinsp;0.17) did not yield significant associations.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eUrinary Lead and PC\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eUrinary lead concentrations remained significantly associated with PC after adjustment for demographic factors (Table S4). Adjustment for age yielded an OR of 0.31 (95% CI: 0.04\u0026ndash;0.57; p\u0026thinsp;=\u0026thinsp;0.03), while PSA adjustment resulted in an OR of 0.31 (95% CI: 0.03\u0026ndash;0.58; p\u0026thinsp;=\u0026thinsp;0.03). Combined adjustment for age and PSA strengthened the association (OR\u0026thinsp;=\u0026thinsp;0.33; 95% CI: 0.04\u0026ndash;0.61; p\u0026thinsp;=\u0026thinsp;0.02). Borderline significance was observed for adjustments involving hematological parameters and inflammatory indices, with p-values ranging from 0.06 to 0.08.\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographics and CBC parameters for BPH, PL, and PC subjects. PSA\u0026thinsp;=\u0026thinsp;prostate specific antigen; WBC\u0026thinsp;=\u0026thinsp;white blood cells; RBC\u0026thinsp;=\u0026thinsp;red blood cells: HGB\u0026thinsp;=\u0026thinsp;haemoglobin; LUC\u0026thinsp;=\u0026thinsp;large unidentified cells; PLT\u0026thinsp;=\u0026thinsp;platelets; MPV\u0026thinsp;=\u0026thinsp;mean platelet volume; PCT\u0026thinsp;=\u0026thinsp;plateletcrit; MPR\u0026thinsp;=\u0026thinsp;mean platelet volume to platelet count ratio; NLR\u0026thinsp;=\u0026thinsp;neutrophils to lymphocytes ratio; MLR\u0026thinsp;=\u0026thinsp;monocytes to lymphocytes ratio; SIRI\u0026thinsp;=\u0026thinsp;systemic inflammation response index.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBPH (n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePL (n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePC (n\u0026thinsp;=\u0026thinsp;78)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68 (64\u0026ndash;75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69 (65\u0026ndash;72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 (69\u0026ndash;77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal PSA (ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.9 (3.3\u0026ndash;9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.4 (1.2\u0026ndash;6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5 (5.4\u0026ndash;14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWBC (10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRBC (10\u003csup\u003e6\u003c/sup\u003e/\u0026micro;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHGB (g/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (14\u0026ndash;16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (14\u0026ndash;15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (13\u0026ndash;15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutrophils (10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymphocytes (10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.1 (1.6\u0026ndash;2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9 (1.6\u0026ndash;2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9 (1.5\u0026ndash;2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonocytes (10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5 (0.4\u0026ndash;0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5 (0.4\u0026ndash;0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4 (0.4\u0026ndash;0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLUC counts (10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1 (0.1\u0026ndash;0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1 (0.1\u0026ndash;0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1 (0.1\u0026ndash;0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLT (10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e225\u0026thinsp;\u0026plusmn;\u0026thinsp;52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e218\u0026thinsp;\u0026plusmn;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e218\u0026thinsp;\u0026plusmn;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMPV (fL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.8 (8.2\u0026ndash;9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCT (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9 (1.6\u0026ndash;2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04 (0.03\u0026ndash;0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042\u0026thinsp;\u0026plusmn;\u0026thinsp;0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04 (0.03\u0026ndash;0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (1.6\u0026ndash;2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22 (0.18\u0026ndash;0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSIRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94 (0.68\u0026ndash;1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1 (0.83\u0026ndash;1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92 (0.75\u0026ndash;1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBlood and urine heavy metal concentrations (unit). BPH\u0026thinsp;=\u0026thinsp;benign prostatic hyperplasia; PL\u0026thinsp;=\u0026thinsp;pre-cancerous lesion; PC\u0026thinsp;=\u0026thinsp;prostate cancer; KW\u0026thinsp;=\u0026thinsp;Kruskal-Wallis test.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePatient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePlasma (\u0026micro;g/L)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKW test\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUrine (\u0026micro;g/L)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKW test\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eT or U test\u003c/p\u003e\n \u003cp\u003e(plasma Vs. urine)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eManganese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBPH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.1 (1.7\u0026ndash;3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (2\u0026ndash;4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.3 (2\u0026ndash;3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.845\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eCobalt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBPH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24 (0.23\u0026ndash;0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27 (0.19\u0026ndash;0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25 (0.23\u0026ndash;0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29 (0.2\u0026ndash;0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eVanadium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBPH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047\u0026thinsp;\u0026plusmn;\u0026thinsp;0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06 (0.04\u0026ndash;0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.072\u0026thinsp;\u0026plusmn;\u0026thinsp;0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06 (0.04\u0026ndash;0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06 (0.04\u0026ndash;0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06 (0.04\u0026ndash;0.098)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eIron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBPH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1227 (880\u0026ndash;1631)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (12\u0026ndash;33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1095\u0026thinsp;\u0026plusmn;\u0026thinsp;387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1236 (812\u0026ndash;1615)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (13\u0026ndash;32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eCopper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBPH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e839 (762\u0026ndash;911)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (7.3\u0026ndash;13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e843\u0026thinsp;\u0026plusmn;\u0026thinsp;121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e862 (730\u0026ndash;988)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (8.8\u0026ndash;16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eZinc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBPH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1695 (1325\u0026ndash;2665)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e518 (357\u0026ndash;695)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2141\u0026thinsp;\u0026plusmn;\u0026thinsp;790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e511\u0026thinsp;\u0026plusmn;\u0026thinsp;280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1639 (1286\u0026ndash;2444)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e508\u0026thinsp;\u0026plusmn;\u0026thinsp;260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eSelenium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBPH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112\u0026thinsp;\u0026plusmn;\u0026thinsp;26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (19\u0026ndash;44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105\u0026thinsp;\u0026plusmn;\u0026thinsp;28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u0026thinsp;\u0026plusmn;\u0026thinsp;21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115\u0026thinsp;\u0026plusmn;\u0026thinsp;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (22\u0026ndash;42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eArsenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBPH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9 (0.54\u0026ndash;3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48 (17\u0026ndash;143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93 (0.4\u0026ndash;3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (17\u0026ndash;151)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99 (0.52\u0026ndash;3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (9.3\u0026ndash;102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eMolybdenum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBPH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94 (0.7\u0026ndash;1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42 (27\u0026ndash;78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91 (0.63\u0026ndash;1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (21\u0026ndash;57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98 (0.76\u0026ndash;1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (27\u0026ndash;64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eCadmium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBPH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01 (0.01\u0026ndash;0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41 (0.24\u0026ndash;0.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01 (0.01\u0026ndash;0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01 (0.01\u0026ndash;0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39 (0.27\u0026ndash;0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eAntimony\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBPH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04 (0.03\u0026ndash;0.078)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.045 (0.03\u0026ndash;0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04 (0.03\u0026ndash;0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eBarium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBPH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1 (0.8\u0026ndash;1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3 (0.68\u0026ndash;2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9 (1.2\u0026ndash;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.77\u0026ndash;1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5 (0.77\u0026ndash;2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eMercury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBPH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64 (0.43\u0026ndash;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.33\u0026ndash;2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52 (0.32\u0026ndash;1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93 (0.49\u0026ndash;1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65 (0.36\u0026ndash;1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72 (0.37\u0026ndash;1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eLead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBPH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1 (0.22\u0026ndash;1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97 (0.63\u0026ndash;2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82 (0.17\u0026ndash;1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1 (0.2\u0026ndash;2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4 (0.83\u0026ndash;2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnadjusted individual metal probit regression models for the association of heavy metal concentrations and PL. OR\u0026thinsp;=\u0026thinsp;odds ratio.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBPH Vs. PL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnadjusted\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eManganese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCobalt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVanadium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eIron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCopper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eZinc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSelenium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eArsenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMolybdenum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCadmium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-14.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAntimony\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eBarium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMercury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u003c/p\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnadjusted individual metal probit regression models for the association of heavy metal concentrations and PC. OR\u0026thinsp;=\u0026thinsp;odds ratio.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBPH Vs. PC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnadjusted\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eManganese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCobalt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVanadium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e0.08\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eIron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCopper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e0.06\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eZinc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSelenium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eArsenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMolybdenum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCadmium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-19.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAntimony\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eBarium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMercury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eComparative Analysis of Metal Concentrations in Prostate Disorders: Insights into Biomarkers and Disease Progression\u003c/h3\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe statistical analysis of metal concentrations in benign prostatic hyperplasia, precancerous lesions, and prostate cancer groups was conducted using the Mann-Whitney U test, a non-parametric method appropriate for comparing independent groups where data may not follow a normal distribution. The test allowed pairwise comparisons between BPH vs. PL, BPH vs. PC, and PL vs. PC, identifying statistically significant differences in metal concentrations across the groups. The box plots illustrate the distribution of these metal concentrations in serum, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, with medians, interquartile ranges (IQR), and whiskers representing the full range of data. A gray zone indicating the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for each group further facilitated comparison across the clinical conditions. The results revealed that plasma manganese concentrations showed a marginally significant difference (p\u0026thinsp;=\u0026thinsp;0.049) in the PC group compared to BPH and PL, suggesting a potential, but inconclusive role in prostate cancer progression. No significant differences were observed in urinary manganese concentrations (p\u0026thinsp;=\u0026thinsp;0.919). In contrast, antimony levels were significantly lower in the PC group, aligning with previous findings that associate reduced antimony concentrations with prostate cancer risk. Elevated vanadium levels in the PL group indicate its potential role in precancerous processes, possibly serving as a diagnostic biomarker for early disease stages. For urine samples, manganese concentrations were consistent across groups, while vanadium and copper concentrations were elevated in the PL group. Furthermore, lead concentrations were significantly higher in the PC group, suggesting a link between lead exposure and prostate cancer risk. Importantly, the reference values used for comparison were taken from Tables 11 and 12 of the ISTISAN Report 17/33, which provides diagnostic reference levels for heavy metals in biological samples. These diagnostic reference levels offer a benchmark for evaluating the observed metal concentrations within the context of prostate health and disease. The results underscore the potential of metals such as vanadium and lead as biomarkers for prostate disease progression and highlight the value of using established diagnostic reference levels in interpreting clinical biochemistry data, Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe results of this study provide valuable insights into the relationship between heavy metal concentrations in plasma and urine and key components of the clinical continuum linking benign states with precancerous and cancerous prostate pathologies. This continuum was further supported by the observed differences in median age and total PSA concentrations across the three groups. Additionally, the reduction in haemoglobin concentrations and the increase in the monocyte-to-lymphocyte ratio (MLR) in PL and PC patients can be interpreted as indicators of potential immunological alterations in these groups, as well as anaemia associated with preneoplastic or neoplastic states. The MLR is a measure of the balance between the body's innate immune response (monocytes) and adaptive immune response (lymphocytes). An increased MLR may indicate, in the context of cancer, a shift from adaptive immunity (which relies on specific immune responses to pathogens) to innate immunity (which provides rapid and nonspecific defence against pathogens) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Additionally, a high MLR may also reflect an ongoing inflammatory response, which is common in cancer and precancerous conditions [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. While these changes provide insights into the body's defense mechanisms, they also highlight the complexity of the immune response in cancer patients.\u003c/p\u003e\u003cp\u003eThe evaluation of heavy metal concentrations revealed significant differences for antimony, with lower plasma levels associated with PC, while manganese showed marginally significant differences in plasma across groups, suggesting potential roles of these metals in the pathophysiology of prostate diseases.\u003c/p\u003e\u003cp\u003eAccording to the International Agency for Research on Cancer (IARC), antimony trioxide (Sb\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e) has been classified as a possible carcinogenic to humans (Group 2B), while antimony trisulfide (Sb\u003csub\u003e2\u003c/sub\u003eS\u003csub\u003e3\u003c/sub\u003e) has not been classified with respect to its carcinogenicity (Group 3) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. There are indications of neoplastic or preneoplastic effects of antimony species in animal models across various tissues and organs. Two recent studies suggested that antimony acts as an endocrine disruptor through interactions with oestrogen and androgen receptors, potentially promoting tumour growth in the reproductive system, although its relevance for \u003cem\u003ein vivo\u003c/em\u003e carcinogenesis in rodents and humans remains unclear [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe observed marginal significance for plasma manganese levels in PC aligns with its known roles in cellular metabolism and oxidative stress. However, further studies are needed to confirm its association with prostate cancer risk and its potential as a biomarker. The absence of significant differences in urinary heavy metal concentrations across the groups suggests that the potential impact of heavy metals on prostate health and disease states is more closely related to systemic exposure rather than renal excretion patterns [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e Regression analysis provided additional evidence for the association between heavy metal concentrations and the presence of PL and PC. Plasma vanadium concentrations were significantly higher in the PL group, with a similar but non-significant trend observed in PC patients. This finding suggests that vanadium may play a role in the early stages of prostate neoplasia. In the literature, the pro-tumor effect of vanadium refers to the potential adverse effects of vanadium exposure on tumor progression and/or the promotion of cancerous conditions [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough vanadium has been studied for its anti-tumor properties, the lack of a significant association with PC in this study indicates that its role might be more relevant in precancerous states [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The analysis also revealed a trend toward a significant association between urinary copper concentration and PC. Variations in copper concentrations or copper/zinc ratios (Cu/Zn) are associated with several tumors, including those of the bladder, breast, colon-rectum, and prostate [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. However, in this study, copper showed only weak trends toward significance, suggesting its limited utility as a biomarker in the current cohort.\u003c/p\u003e\u003cp\u003eAnother interesting finding concerns the significant association between urinary lead concentration and PC. Previous studies have suggested a potential link between environmental lead exposure and the risk of developing prostate diseases [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Higher blood lead concentrations have been reported in PC cases, suggesting that environmental lead exposure may influence prostate pathology risk. The consistent association of urinary lead with PC, even after adjustment for confounders, highlights its potential as a robust biomarker for prostate cancer risk [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdjusting for potential confounding factors such as age, total PSA, and complete blood count (CBC) did not alter the significant association between higher plasma vanadium concentrations and PL, reinforcing the possible role of vanadium in prostate health. However, the trend toward an association between copper concentration and PC did not persist after adjustment, suggesting that its role might be secondary or confounded by other variables [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGlobally, these results underscore the importance of exploring plasma antimony and vanadium as potential biomarkers for prostate pathologies, while highlighting the need for further research to confirm the roles of manganese, copper, and lead in prostate cancer progression [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA final remark should be dedicated to the comparison of our results to those present in the literature for the same type of cancer [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The heterogeneity in the type of biological samples used for the determination of metal concentrations (serum, tissue, hair and nails, blood and urine) and in the patients enrolled, unfortunately, does not allow us any sound comparison and conclusion. We can only report that in general the metals showing altered concentrations in the case of PC are mainly zinc, selenium, arsenic, cadmium, iron, lead, and to a lesser extent copper, manganese, mercury and antimony. Especially zinc seems to have a protective role against development of PC, as confirmed in different studies where PC patients show low levels of this essential metal. In ours, zinc does not correlate to any prostate pathology. Instead, we have found strong evidence that vanadium is, while the involvement of this element was never reported before. This indicates that further studies are needed to clarify such correlations, together with the need of a common and shared methodology for a better comparison of the results.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eMale subjects with prostate cancer, precancerous lesions, or benign prostatic hypertrophy were recruited from the Urology Department of the University Hospital of Sassari. A total of 78 cases (PC and PL) and 76 controls (BPH) were initially recruited, meeting the calculated sample size required for sufficient statistical power.\u003c/p\u003e\u003cp\u003eThe power calculation was based on an expected mean difference (ΔΔ) of 5 \u0026micro;g/L\u0026micro;g/L (difference in metal levels between cases and controls) and a standard deviation (σσ) of 10 \u0026micro;g/L\u0026micro;g/L, as derived from preliminary data and literature. Using these parameters, the study achieved\u0026thinsp;\u0026gt;\u0026thinsp;80% power to detect a medium effect size (Cohen's d\u0026thinsp;=\u0026thinsp;0.5) at a significance level of α\u0026thinsp;=\u0026thinsp;0.05/3, adjusted for Bonferroni correction for the three tested metals.\u003c/p\u003e\u003cp\u003eHowever, due to incomplete data or missing measurements for some metals, the final analysis included data from 61 controls (BPH). Despite this limitation, the available data ensured sufficient power (\u0026gt;\u0026thinsp;75%) to detect the expected effect size, supporting the validity of this exploratory biomarker study.\u003c/p\u003e\u003cp\u003e The study was conducted in accordance with the Declaration of Helsinki and was approved by the Independent Ethics Committee of the University Hospital of Cagliari (P.O. San Giovanni di Dio: via Ospedale 54\u0026ndash;09124 Cagliari) under Approval Code Prot. PG/2022/4985, dated March 30, 2022. Written informed consent was obtained from all participants prior to their inclusion in the study.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCriteria\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eExclusion from the study was based on factors that may influence metal concentration: a previous diagnosis of PC, other cancers, or a subsequent diagnosis of metastatic PC after biopsy. Patients were included if they had a positive screening test (abnormal Digital Rectal Examination (DRE), high age-specific serum Prostate-Specific Antigen (PSA) concentration, or serum PSA of \u0026gt;\u0026thinsp;0.35 ng/mL per year). A questionnaire capturing demographic information, medical history, family history of cancer, PC screening, urological health, and lifestyle factors (e.g., smoking and physical activity) was used in each participant. Ultrasound biopsy was performed to determine the following groups: i) BPH; ii) high-grade prostatic intraepithelial neoplasia or prostatic atypical small acinar proliferation/atypia (PL); and iii) PC.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHeavy Metals Serum Analysis Using ICP-MS\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTotal metals and non-metals in plasma (aluminium, silver, beryllium, chromium, lithium, nickel, tin, thallium, manganese, cobalt, vanadium, iron, copper, zinc, selenium, arsenic, molybdenum, cadmium, antimony, barium, mercury and lead) and urine (antimony, arsenic, cadmium, manganese, molybdenum, lead, copper, selenium, tin, thallium, zinc) were determined by inductively coupled plasma mass spectrometry (ICP-MS) in accordance with the US EPA 6020B method [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Biological fluids were analysed directly after dilution of 0.5 mL of sample in 5 mL with 2% nitric acid (J.T. Baker, Phillipsburg, NJ, USA) solution. The analysis was performed with an inductively coupled plasma mass spectrometer ICP-MS/MS (Agilent 8800 QQQ, Santa Clara, CA, USA) equipped with a collision cell and two quadrupole mass analyzers. In comparison to a single quadrupole ICP-MS system, the triple quadrupole system significantly increases the accuracy of mass separation. To compensate for the matrix effect and signal drift, a solution of internal standards was used. The calibration curve was verified at the start of each analytical batch using the initial calibration verification (ICV) with a different lot standard, while the instrumental sensitivity was verified using the continuous calibration verification (CCV) at or near midrange. The LOQs testing was 0.001 ng/mL for all elements analysed. The quality control of the data was verified and controlled using Certified Reference Materials ClinChek\u0026reg; Urine Control, and ClinChek\u0026reg; Plasma Control for Trace Elements, (RECIPE Chemicals, M\u0026uuml;nchen). Laboratory was intercalibrated through successful participation in internationally organized proficiency tests (OELM). The method is accredited according to UNI EN ISO 17025/2017 [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDifferences in subject characteristics, including age, ethnicity, education, marital status, family cancer history, were compared between PC cases and BPH or PL using the chi-squared test. To ensure that there was no selection bias when samples for the assay were chosen, the chi-squared test was used to compare background characteristics of the corresponding subjects between groups. The Shapiro-Will test of normality was used to test the statistical distribution of each variable. Data are presented as mean and standard deviation (SD) or median and interquartile range (IQR), and T or U test were used accordingly to test for the presence of any difference between two parametric or non-parametric distributions. The Kruskall-Wallis test was used to assess differences of more than two non-parametric distributed variables. Probit regression models to estimate odds ratio (OR) with 95% confidence intervals (CI) for univariate linear regression analysis of each heavy metal concentration in plasma and urine, and its association with pre-cancerous lesion or prostate cancer. The models were also adjusted in bivariate linear regression analyses for age, total PSA, and the following haematological parameters: WBC, RBC, HBG, neutrophils, lymphocytes, monocytes, and LUC). Statistical analyses were performed using Stata 14 (STATA Corp., College Station, TX, USA). The R heatmap package was used to create the heatmaps.\u003c/p\u003e \u003cp\u003eThe inclusion of haematological parameters (WBC, RBC, HBG, neutrophils, lymphocytes, monocytes, and LUC) as confounders in the regression models was driven by their established relevance in the pathophysiology of prostate diseases. Chronic inflammation plays a central role in prostate carcinogenesis, and WBC levels, as a marker of systemic inflammation, can influence tumour progression and the tumour microenvironment. Similarly, RBC and HBG are indicative of anaemia or disruptions in erythropoiesis, which are frequently observed in cancer patients and can affect the systemic transport and bioavailability of heavy metals. Parameters such as neutrophils and lymphocytes, key components of the immune response, are closely tied to the inflammatory milieu and immune modulation in cancer. By adjusting for these variables, the analysis aimed to reduce confounding effects and isolate the specific associations between heavy metal concentrations and prostate pathology. These adjustments improve the robustness of the findings by accounting for potential interactions between systemic inflammation, haematological status, and metal bioaccumulation.\u003c/p\u003e \u003c/div\u003e "},{"header":"Conclusions","content":" \u003cp\u003eThis study provides compelling evidence of the complex interaction between heavy metal concentrations and the risk of developing prostate diseases. The significant associations between plasma vanadium concentrations and the risk of PL and PC, as well as the potential protective role of plasma antimony against PC, highlight the need for further research to clarify the underlying mechanisms and explore the potential of heavy metals as biomarkers or therapeutic targets for prostate health. The results also emphasize the importance of considering systemic exposure to heavy metals in the context of prevention and management strategies for prostate diseases.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This project and D.C. were supported by the Autonomous Region of Sardinia, pursuant to Regional Law August 7, 2007, No. 7 \u0026quot;Promotion of Scientific Research and Technological Innovation in Sardinia - Project UGOV RAS_CRP2023 CARRU: \u0026ldquo;Role of Circulating Biomarkers in the Management of Patients Affected by Prostate Carcinoma\u0026rdquo;. National LILT Program - 5 per thousand years 2022, Scientific-Sanitary Research Call LILT 2023: \u0026ldquo;Study of the Heterogeneity of Prostate Carcinoma as a Key for Dissecting Cellular Subtypes That Characterize It\u0026rdquo;, Number: LILT - Protocol Number 2024U0001294 dated March 29, 2024. Se.M. was funded by Fondazione di Sardegna, MEDICI 2017 Project \u0026quot;Nuclear Magnetic Resonance Studies: Toxic Metals and Carcinogens and Their Interactions with Cellular Targets\u0026quot; and MEDICI 2024 Project \u0026ldquo;Metalli Pesanti e Tumori: Comprendere l\u0026apos;Impatto Ambientale sulla Salute Umana\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u0026nbsp;\u003c/strong\u003eThe study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee COMITATO ETICO INDIPENDENTE - Azienda Ospedaliero Universitaria di Cagliari P.O. San Giovanni di Dio: via Ospedale 54 \u0026ndash; 09124 Cagliari. Approval Code: Prot. PG/2022/4985. Ap-proval Date: 30/03/2022.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u0026nbsp;\u003c/strong\u003eWritten informed consent has been obtained from the patients to publish this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u0026nbsp;\u003c/strong\u003eThe datasets generated and analyzed during this study are available from the corresponding author upon reasonable request. Researchers seeking access may be required to provide a brief proposal outlining the intended use and agree to confidentiality terms as per institutional policies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information:\u003c/strong\u003e The authors declare no competing financial, professional, or personal interests that could influence the performance or presentation of this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRawla, P. 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(2017).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"biomarkers, prostate cancer, heavy metal, cancer risk, inductively coupled plasma mass spectrometry (ICP-MS)","lastPublishedDoi":"10.21203/rs.3.rs-5822110/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5822110/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHeavy metals influence the development of several health conditions, including inflammation and cancer. This study investigates the relationship between heavy metal concentrations in plasma and urine, and the presence of benign prostatic hyperplasia (BPH), precancerous lesions (PL), and prostate cancer (PC). The influence of age, total PSA levels, hemoglobin concentrations, and the monocyte-to-lymphocyte ratio (MLR) is also analyzed across the three groups: BPH, PL, and PC. Our findings reveal significant differences in vanadium and antimony concentrations in plasma, suggesting a potential role in prostate disease pathophysiology. Notably, lower plasma antimony concentrations are associated with an increased risk of PC, while plasma vanadium concentrations are significantly higher in the PL group. Regression analysis further supports the association between heavy metal concentrations and the risk of PL and PC, highlighting the potential of vanadium and copper as biomarkers or therapeutic targets for prostate health. The study also explores the impact of lead exposure on prostate cancer risk, revealing a significant association between urine lead concentration and PC. These findings underscore the complex interaction between heavy metal concentrations and prostate disease risk, emphasizing the need for further research to elucidate underlying mechanisms and explore therapeutic interventions.\u003c/p\u003e","manuscriptTitle":"Heavy metals and prostate cancer: a new study with new findings","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-16 11:17:04","doi":"10.21203/rs.3.rs-5822110/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-13T07:45:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-10T06:15:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-29T12:58:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"134764092092492169645181296349671401515","date":"2025-01-29T05:39:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302623798586569577219283651590076173210","date":"2025-01-19T04:56:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-17T18:23:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-17T18:20:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-01-14T10:18:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-14T10:15:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-01-13T18:19:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f42d8c9c-44bc-41f2-ab76-bcc4cc618167","owner":[],"postedDate":"January 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":42838576,"name":"Biological sciences/Biochemistry"},{"id":42838577,"name":"Biological sciences/Cancer"},{"id":42838578,"name":"Biological sciences/Chemical biology"},{"id":42838579,"name":"Health sciences/Biomarkers"},{"id":42838580,"name":"Health sciences/Health care"},{"id":42838581,"name":"Health sciences/Oncology"},{"id":42838582,"name":"Health sciences/Pathogenesis"},{"id":42838583,"name":"Health sciences/Risk factors"},{"id":42838584,"name":"Health sciences/Urology"},{"id":42838585,"name":"Physical sciences/Chemistry"}],"tags":[],"updatedAt":"2025-04-28T16:12:59+00:00","versionOfRecord":{"articleIdentity":"rs-5822110","link":"https://doi.org/10.1038/s41598-025-97682-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-04-24 15:57:31","publishedOnDateReadable":"April 24th, 2025"},"versionCreatedAt":"2025-01-16 11:17:04","video":"","vorDoi":"10.1038/s41598-025-97682-0","vorDoiUrl":"https://doi.org/10.1038/s41598-025-97682-0","workflowStages":[]},"version":"v1","identity":"rs-5822110","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5822110","identity":"rs-5822110","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}